The present invention relates to improvements to crossplot analysis of A.V.O. anomalies in seismic surveying and, more particularly, but not exclusively to apparatus and a method for identifying an A.V.O. anomaly indicative of the presence of oil or gas, via an improved A.V.O. crossplot.
Seismic surveying is the basic technology used for imaging the subsurface for oil exploration. Seismic surveying involves the transmission of sound energy into the subsurface and recording the echoes that are reflected from the boundaries between the layers in the subsurface. The recorded signal is processed and displayed as seismic sections. FIG. 1 is an example of a typical seismic section in which the vertical axis represents depth and the horizontal axis represents surface distance. The seismic section in FIG. 1 is a vertical cross-section of the sub-surface, showing layers 10, faults 12, folds 14 and other subsurface features. Using such sections a geologist is able to see the geometry of the layers at depth, and can suggest likely locations for drilling test bores to find oil or gas.
In addition to the geometry of the layers, the seismic section can obtain a certain amount of information on the composition of the rocks in the subsurface by analyzing the amplitude (the strength) of the reflected signal. The amplitude or strength of the reflected signal is governed by the difference in the physical properties between the layers respectively above and below the boundary causing the given reflection. A typical boundary is shown schematically in FIG. 2. A layer atop the boundary is composed of rocks having a first density and elasticity values, and a layer below the boundary is composed of rocks having a second density and second elasticity values. The differences between the densities and the elasticity values contribute to the amplitude of the reflected signal and thus carry information about the physical properties of the layers.
In standard seismic surveying each subsurface point is illuminated from different directions, that is to say illumination angles, each direction producing a seismic section (constant angle section). As the sections show the same slice they can be superimposed or stacked. Stacking of the sections serves to de-emphasize differences between the sections whilst similarities are enhanced. As the differences are most likely noise the stacking procedure tends to enhance the signal to noise ratio. The stacking procedure is known in the art as CMP stacking, and is a standard method of enhancing the Signal to Noise ratio.
The multi-directional illumination enables analysis of the amplitude of reflection at a given point as a function of the angle of illumination. Such analysis is carried out before stacking the data. Normally, when doing so, it is apparent that the amplitude decreases with increasing angle. That is to say, the greater the angle of illumination and consequently of reflection, the smaller the amplitude of the received signal. About 20 years ago it was discovered that when gas (and sometimes oil) is present in the layer, the amplitude behaves in an anomalous way, and in fact tends to increase as a function of the angle of illumination, and likewise of the reflection angle. The increasing amplitude phenomenon is known as an A.V.O. anomaly. A.V.O. is an abbreviation for Amplitude Versus Offset.
Reference is now made to FIG. 3, which is a schematic diagram illustrating a vertical section through the ground having a shale layer, a gas—sand layer, and another shale layer. Alongside each of the layer boundaries are shown a series of seismic traces illustrating signal amplitudes as the reflection angle increases to the right. The figure illustrates a basic A.V.O. model in the case of reflection from a gas—sand layer boundary. In the figure, σ—Poisson's ratio, is given for each layer as representative of the elastic properties of the rock. As will be seen, at the upper, shale—gas, boundary, the amplitude increases from small negative to large negative, and at the lower, gas—shale, boundary, the amplitude increases from low positive to high positive.
A.V.O. anomalies are usually categorized into 4 different classes, three of them are illustrated in FIG. 4, which shows for each class the changes in amplitude of a received signal as the reflection angle increases to the right:
Class I: Amplitude at zero angles is positive, and it becomes smaller as the angle increases.
Class II: Amplitude at zero angles is close to zero, and it becomes more negative as the angle increases.
Class III: Amplitude at zero angles is negative, and it becomes more negative as the angle increases.
Class IV: Amplitude at zero angles is negative, and it becomes larger (more positive) as the angle increases.
Identification of A.V.O. anomalies has become a very important tool in oil and gas exploration. When an A.V.O. anomaly exists, it is a strong indication of the presence of gas. An A.V.O. anomaly is not an absolute guarantee of oil or gas but it is sufficient to provide a very significant impact on the drilling success rate.
The identification of A.V.O. anomalies within the seismic data is not a simple task. Seismic 3-D surveys are very large and contain gigabytes and even terabytes of data. To perform A.V.O. analysis it is necessary to determine how the amplitude at each subsurface point behaves as a function of the reflection angle. In classic seismic processing (not for AVO purposes), the process of stacking serves to average out the amplitude and reduces the amount of information by a great amount. For AVO analysis, stacking is not an option as we seek to see the changes of amplitude before stacking. Instead of inspecting each AVO gather (data at a single surface location as a function of reflection angle) a lengthy and complex process, it is common to create what are known as A.V.O. attributes. A.V.O. attributes normally measure two A.V.O. parameters: The amplitude at zero reflection angles (Normal Incidence—NI), and the rate of change of amplitude as the reflection angle changes (Gradient—G). A.V.O. anomalies can be directly identified using these two attributes. Reference is now made to FIG. 5, which illustrates side by side a plot of amplitude at zero reflection angles (NI), on the left (a) and of gradient on the right (b).
In FIG. 5 color coding is used to indicate the amplitude of the signal. White indicates small amplitude. Yellow to orange are normal amplitudes, red indicates large positive amplitude and blue indicates large negative amplitude. The presence of an A.V.O. anomaly is indicated wherever large amplitudes on the gradient graph b) correspond to small amplitudes on the NI graph a). The region marked by the black circle is such a region. Other regions of high gradient in b) correspond to high NI on the amplitude graph a) and therefore are disregarded. The region marked by the circle is the phenomenon that interests geologists. It indicates an A.V.O. anomaly and thus a high probability of the presence of gas.
Double plots of the kind shown in FIG. 5 can disclose A.V.O. anomalies, howeverdue to the size of typical surveys it is not practical for the matching to be carried out manually by simple inspection. A geologist may often miss an AVO anomaly when inspecting dual attribute datasets. Automated techniques are called for to enable the analysis of all AVO anomalies that are present in the dataset, and classify them according to the standard AVO classifications. Instead a different technique known as A.V.O. crossplotting is used in standard automated or partly automated processes for identifying A.V.O. anomalies within a 2-D or 3-D seismic dataset. Crossplotting is a mathematical mapping process that can easily be performed by computer and it provides a visual output. In AVO crossplotting, the same two A.V.O. attributes as used in the double plot, namely NI and G, may be used. Each subsurface point is mapped uniquely into a point in crossplot space, which is simply a two-dimensional space having, as axes, NI and G. Mapping of AVO attribute data into the crossplot space is done as follows: For a given subsurface point, the amplitude (a) of the data point on the NI attribute is extracted; also the amplitude (b) from the G attribute is extracted. The data is then mapped onto a single point (a, b) in the crossplot space.
Reference is now made to FIG. 6, which is a simplified diagram illustrating a crossplot space onto which a point (a,b), representing an NI value of a and a G value of b has been plotted. Mapping onto such a space transforms each class of an AVO anomaly onto a unique part of the crossplot space. Thus, regions in the crossplot space are uniquely associated with a specific AVO class, or of course with no class at all, for examplemud and rocks.
AVO crossplotting is a very useful tool for classifying and mapping AVO anomalies. However, one disadvantage is that, unlike the double plotting of FIG. 5, the crossplot itself loses the location information of the point mapped. Thus, in order to make successful use of the crossplot, it is necessary to map AVO data firstly to the crossplot space as described above so that it can be categorized into its anomaly class or no anomaly, as appropriate. Then, once the point is categorized, the categorization is applied to the location from which the point is taken so that true subsurface location can be determined. Such a procedure is typically performed as part of a computer program, which maps from the crossplot space back to the attribute data, after the data has been classified in the crossplot space.
Ideally, it is possible to map each type of AVO anomaly to the identified locations in the crossplot space as shown in FIG. 7, which is an idealized version of the cross plot space, showing the various regions that correspond with the class 1–4 anomalies. The non-AVO anomaly data maps onto a line, the so-called no-oil or mud rock line which extends through the origin from upper left to lower right. The line is of negative slope to represent amplitude changes that decrease with reflection angle. Based on a-priori knowledge of how AVO anomalies map in the crossplot space, as represented in FIG. 7, it is possible to classify all seismic data points according to a corresponding AVO signature. Using AVO crossplotting, it is possible to map all AVO anomalies in 3-D. By following the above procedure, it is possible to provide a categorization for each data point automatically, even in a very large terabyte range 3-D survey.
The problem with Conventional AVO Crossplotting
Unfortunately, reality is not as kind as FIG. 7 implies. Reference is now made to FIG. 8, which is a graph showing how an A.V.O. crossplot appears for a real large data set. In reality the separation of AVO anomalies from the rest of the data using AVO crossplotting does not work in most cases. That is to say the data does not cluster around the different regions, but rather forms a difficult to classify continuum. FIG. 8 is a crossplot of Normal Incidence (NI) versus Gradient (G) created from a 3-D dataset recorded over a large gas field. All the data is concentrated together and there is no way of reliably discriminating between AVO and non-AVO effects. In other words there is no formation of recognizable clusters that can be separated from one another.
Reference is now made to FIG. 9, which shows the regions of FIG. 7 superimposed upon the data of FIG. 8. In the dataset represented in FIGS. 8 and 9, there is inter alia a class II AVO anomaly. FIG. 10 shows the result of selecting the part of the data associated with the class II AVO, that is the data within the circle II, and tracing it back to the double graphs of FIG. 5 by highlighting. It can be seen from FIG. 10 that indeed the A.V.O. zone is identified correctly, but many points outside the anomaly are also marked.
Furthermore, it is not clear, simply from looking at FIG. 9 that a type II A.V.O. anomaly actually exists in the data. All points which are pink in FIG. 9 are marked pink on the attributes in FIG. 10. The situation illustrated in FIG. 10 is a very typical situation. The reason for this behavior is discussed in several papers and is explained well by Ross, 2000 and by Keho, 2000, the contents of which are hereby incorporated by reference.
Reference is now made to FIG. 11, which is a simplified diagram showing an attribute only graph a) next to a cross-plot b) and illustrating how the situation in FIG. 10 in fact arises. A rectangle at the origin of a) represents data from a class II anomaly. The data from within the rectangle in a) that is to say data of the clear class II AVO anomaly, is mapped onto the crossplot b). It would be expected from the analysis of FIG. 7 above that all of the data from within the rectangle is mapped to the class II anomaly region in b) but in fact this is not the case. The points are drawn in purple over the crossplot b). FIG. 11b clearly shows that in fact very little of the AVO data maps into the class II zone. Rather most of the purple points are distributed throughout the main cluster of data points and in fact seem to form up substantially about a straight line crossing the origin and having a negative slope.
To understand how AVO anomalies actually map to an NI-G crossplot, it is necessary to consider the effect of the original wavelet from which the imaging data is obtained and the effect of wavelet distortions with varying reflection angle. The wavelet is the shape of the source signal. A reflection from a subsurface interface is not a point reflection because the seismic signal has length in time. Hence, each reflection is contaminated by the shape of the source function—the wavelet. When taking into account the effect of the wavelet, a single NI-G event will not in fact map according to what is described in FIGS. 6 and 7, which turn out to be highly simplified. The event actually maps, in an ideal case, to a line in the crossplot space, as illustrated in FIG. 12a to which reference is now made. The slope of the line distinctively defines an AVO signature. FIG. 12b shows different points along the length of a wavelet which are all associated with a single AVO event. When these points are mapped to a crossplot as in FIG. 12a, they in fact form the line referred to above and shown in FIG. 12a. 
Each type of AVO anomaly has a different slope, and the “No Oil” line is also mapped distinctively to a specific line in the crossplot space as shown in FIG. 13. FIG. 7 turns out to be an idealized picture that does not apply due to wavelet effects. A more realistic description of the AVO crossplot space and what is happening in practice is illustrated in FIG. 13. In theory, then classification of data points according to the layout of FIG. 13 should provide an improved way of recognizing A.V.O.s.
A further complication of the above-described situation, and part of the reason that the data does not cluster around the lines of FIG. 13, is associated with wavelet variations as a function of reflection angle. The wavelet variations lead to distortions, which affect the AVO attributes and typically distort the simplified picture, causing a scatter of points around the lines of FIG. 13. The lines as illustrated in FIG. 13 thus become no more than a basic trend for the data points rather than being a line on which the data points sit. The situation is illustrated in FIG. 14, which may be viewed as a more realistic version of FIG. 12. FIG. 14a shows a crossplot of points shown in the wave amplitude mapping of FIG. 14b. It will be noted that in FIG. 14b the Normal Incidence signal is different from the Gradient signal, and this is due to the above-described distortion. When mapping to the crossplot space of FIG. 14a the distortion leads to point scatter around the basic trend line. The extent of the scattering is such that it is difficult to resolve between the different trend lines, and thus recognizable clustering is not seen.
FIG. 15 is a crossplot based on a real data set. It shows an AVO class I anomaly colored pink superimposed on the full data shown in blue. It is clear that the AVO data has a different trend from the total data, that is to say it forms up along a different axis, and in fact the general data trends along the no-oil line whereas the class I anomaly data trends along the class I line. However, be that as it may, the two datasets in the crossplot space of FIG. 15 cannot be separated because they do not form distinct clusters. Without the superimposed color coding, which is to say without prior knowledge, there is no way that the two trends could be spotted from the crossplot.
FIG. 16 is another example taken from the same data set as FIG. 15, but illustrating a Class II AVO anomaly. Again when color coded it is easy to see that the data lines up along two different trend lines, but without prior knowledge there is no way of identifying the trends.
FIG. 17 is a similar display of non-AVO data. Here known non-anomaly data is colored pink whereas general (unclassified) data is left blue. It is clear that the non-AVO data aligns with the general trend of the background data.
During a conventional crossplot procedure, each data point is mapped into a single point on the crossplot space. Consequently, each AVO event, which spans across a number of data points, is mapped to a number of locations in the crossplot space. Hence, theoretical division of the crossplot space to different AVO regions turns out to be unrealistic because there is no one-to-one mapping of crossplot space to AVO signatures.
There is thus a widely recognized need for, and it would be highly advantageous to have an effective way of identifying AVO anomalies, which can be automated, and takes into account both the wavelet shape and the wavelet variations as a function of reflection angle.